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Found 13 papers, 1 papers with code
Lexical semantic change for Ancient Greek and Latin
In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model.
A Bayesian decision support system for counteracting activities of terrorist groups
Activities of terrorist groups present a serious threat to the security and well-being of the general public.
Constructing a Chain Event Graph from a Staged Tree
This coloured event tree, also known as a staged tree, is the output of the learning algorithms used for this family.
Propagation for Dynamic Continuous Time Chain Event Graphs
Chain Event Graphs (CEGs) are a family of event-based graphical models that represent context-specific conditional independences typically exhibited by asymmetric state space problems.
Bayesian Learning of Causal Relationships for System Reliability
Causal theory is now widely developed with many applications to medicine and public health.
GASC: Genre-Aware Semantic Change for Ancient Greek
Word meaning changes over time, depending on linguistic and extra-linguistic factors.
Properties of an N Time-Slice Dynamic Chain Event Graph
An N Time-Slice DCEG (NT-DCEG) is a useful subclass of the DCEG class that exhibits a specific type of periodicity in its supporting tree graph and embodies a time-homogeneity assumption.
An N Time-Slice Dynamic Chain Event Graph
The Dynamic Chain Event Graph (DCEG) is able to depict many classes of discrete random processes exhibiting asymmetries in their developments and context-specific conditional probabilities structures.
Directed expected utility networks
We then proceed with the construction of a directed expected utility network to support decision makers in the domain of household food security.
A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams
For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation.
Sensitivity analysis, multilinearity and beyond
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensively studied and implemented in different software packages.
A Separation Theorem for Chain Event Graphs
Bayesian Networks (BNs) are popular graphical models for the representation of statistical problems embodying dependence relationships between a number of variables.
Exact Estimation of Multiple Directed Acyclic Graphs
This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models.
